Abstract

Understanding why automatic recommendation systems make decisions is an important area of research because users' satisfaction improves when they understand the reasoning behind the suggestions. In the area of visual art recommendation, explanation is a critical part of the process of selling artworks. Traditionally, artwork has been sold in galleries where people can see different physical items, and artists have the chance to persuade potential customers into buying their work. Online sales of art only offer the user the action of navigating through the catalog, but nobody plays the persuasive role of the artist. In the music industry, another artistic domain, recommendation systems have been very successful and play a key role by showing users what they would like to hear. There is a large body of research about this field of recommendation, but there is little research about explaining content-based recommendations of visual arts, though both belong to the artistic domain. Current works do not provide a perspective of the many variables involved in the user perception of several aspects of the system such as domain knowledge, perception of relevance, explainability, and trust. In this paper we aim to fill this gap by studying several aspects of the user experience with a recommender system of artistic images. We conducted two user studies in Amazon Mechanical Turk to evaluate different levels of explainability, combined with different algorithms, interfaces and devices, in order to learn about the interaction between these variables and which effects cause these interactions in the user experience. Our experiments confirm that explanations of recommendations in the image domain are useful and increase user satisfaction, perception of explainability and relevance. In the first study, our results show that the observed effects are dependent on the underlying recommendation algorithm used. In the second study, our results show that these effects are also dependent of the device used in the study. Our general results indicate that algorithms should not be studied in isolation, but rather in conjunction with interfaces and the device since all of them play a significant role in the perception of explainability and trust for image recommendation. Finally, using the framework by Knijnenburg et al., we provide a comprehensive model, for each study, which synthesizes the effects between different variables involved in the user experience with explainable visual recommender systems of artistic images.